Cognitive computing is the presentation of human thought process in computerized model. It helps machine to think, interpret and conclude like human brain. Using machine learning algorithms that use pattern recognition, data mining, predictive modelling and natural language processing, the machine can replicate the way the human brain works. They help in making better decisions for humans by providing insights derived by crunching, analyzing and finding patterns in large data sets. The purpose of cognitive computing is the creation and adoption of computing frameworks that can solve difficult problems without constant human intervention. Basically cognitive computing is an architecture that makes use of multiple artificial subsystems together. Today cognitive computing is helping in decision making not only in healthcare but also in other industries like finance, customer services, education, research and many more.
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The growth of cognitive computing market in healthcare is due to huge demand of organizations to incorporate advanced data analytics technology into their business processes so as to provide more efficient results and improved patient diagnosis, research and various clinical operations. Moreover there is lot amount of large, unstructured and complex data which needs to be analyzed so as to enhance patient care. This complex data is available in various form such as natural language video, images and many more. Existing big data analytics systems are not able to process these data sets hence business organizations are shifting towards the cognitive computing. However for driving efficient results cognitive computing systems for healthcare should be adaptive and should be able to mimic human brain and surroundings as well as it should be highly interactive and should be able to interact with all systems such as processor, cloud services, devices and user. Artificial intelligence (AI) with the emergence of Internet of things (IoT) is expected to drive the cognitive market which is expected to grow rapidly during the forecast period.
Global cognitive computing in healthcare market can be categorized on the basis of technology such as natural language processing (nlp), machine learning, automated reasoning, and other technologies. Among technology segment natural language processing segment is expected generated the maximum revenue in global cognitive computing in healthcare market and is followed by machine learning. On the basis of deployment model cognitive computing in healthcare market can be categorized into cloud based and on premise platform. On the basis of organization size cognitive computing in healthcare market can be categorized into large enterprises and small and medium sized enterprises also called (SMEs). On the basis of geography, the global cognitive computing in healthcare market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and South America.
Among the deployment model, cloud based cognitive computing in healthcare is expected to rise in forecast period due to adoption of cloud based solution for reducing operational and infrastructure costs and enhancing flexibility. Among the organization size adoption of cognitive computing in healthcare market is going to increase during the forecast period due to emergence of cost effective solutions and cloud based services. Among the cognitive computing in healthcare market, North America is the largest market for global cognitive computing market and is expected to continue its dominance over forecast period. Also Asia-Pacific is expected to become a lucrative market for cognitive computing in healthcare sector as lot of investments are being made in this sector.
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The key players in the SMS firewall market include IBM corporation, Microsoft Corporation, Google Inc., Nuance Communications, Inc., Saffron Technology, Inc., Apple Inc., Dell, Healthcare X.0, MedWhat, Apixio. These companies have invested heavily in the cognitive technology platforms to develop new products catering to the healthcare industry.
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